Kemerovo, Kemerovo, Russian Federation
Kemerovo, Kemerovo, Russian Federation
Kemerovo, Kemerovo, Russian Federation
Kemerovo, Kemerovo, Russian Federation
Kemerovo, Kemerovo, Russian Federation
Introduction. Choline has a wide range of physiological functions. It has a neuroprotective effect on brain dysfunctions, while its deficiency has a negative effect on antenatal development of the nervous system. We aimed to study the impact of exogenous choline on the psychophysiological indicators in students. Study objects and methods. 87 students were surveyed by questionnaire to determine their background intake of dietary choline. One month before the exams, we measured their simple and complex visual-motor reaction times, functional mobility and balance of nervous processes, as well as indicators of their short-term memory, attention, health, activity, and mood. Then, we divided the students into a control and an experimental group, regardless of their choline intake. The experimental group took 700 mg choline supplements on a daily basis for one month, followed by a second psychophysiological examination. Results and discussion. Students with a low choline intake had lower functional mobility and balance of nervous processes, but better attention stability than students with a high choline intake. The second examination showed improved short-term memory, health, and activity indicators in the experimental group, compared to the control. The visual-motor reaction times also increased, but only in students with an initially low level of choline intake. Conclusion. Choline supplementation can be recommended to students under pre-exam stress to enhance the functional state of their central nervous system.
Choline, intake level, choline supplements, students, psychomotor reactions, cognitive functions
INTRODUCTION
There has been a lot of research into choline over the
past few decades. It is a vitamin-like nutrient that takes
part in many physiological processes and has a wide
range of physiological functions [1, 2].
Choline is ingested with food as part of
phosphatidylcholine or formed endogenously. The
human need for choline is met mainly through food.
Its adequate daily intake is 425 mg for women and 550
mg for men, but not more than 3.5 g/day [3]. Metabolic
pathways for the conversion of dietary choline and its
endogenous synthesis are genetically heterogeneous.
This determines individual sensitivity to a deficiency of
choline [4, 5].
Choline has a significant effect on the development
and functioning of the nervous system. As part of
phosphatidylcholine, it participates in the construction,
stabilization, and repair of cell membranes, including
neurons. As a component of sphingolipids, it myelinates
nerve fibers [6, 7]. As a precursor of betaine (a methyl
group donor), choline is a factor in epigenetic regulation
of gene expression during neurogenesis [8, 9]. DNA
methylation is a dynamic process that can modulate the
expression of genes that regulate synaptic plasticity.
Since neurogenesis continues throughout life, dietary
intake of choline as a source of methyl groups can
affect cognitive functions at various stages of ontogenesis
[10].
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Foods and Raw Materials, 2021, vol. 9, no. 2
E-ISSN 2310-9599
ISSN 2308-4057
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Tarasova O.L. et al. Foods and Raw Materials, 2021, vol. 9, no. 2, pp. 397–405
Our special interest is in choline as a precursor of
acetylcholine, the most important neurotransmitter
of the central and peripheral nervous system.
Cholinergic systems of the brain have been in the
center of neuroscientific and medical research due to
their importance for cognitive functions and motor
skills [11–14]. The influence of choline, either
ingested or synthesized endogenously, on the effects
of cholinergic neurotransmission is determined by a
large number of genetic and epigenetic factors. These
factors include enzyme systems that transport choline
to the presynaptic terminals of neurons, the synthesis
of acetylcholine from choline and acetylcoenzyme A
and its inactivation after its use in synapses, as well as
localization and activity of muscarinic and nicotinic
cholinergic receptors. Therefore, it is difficult to
interpret experimental data on the relationship between
exogenous choline and the effects of acetylcholine.
The effects of choline on the nervous system
have also been extensively studied. For example, its
deficiency has a negative impact on the intrauterine
development of the nervous system. Some studies
on animals found that choline-enriched nutrition of
pregnant females improved the cognitive functions
of their offspring at various stages of ontogenesis and
slowed down age-related involution. Its most pronounced
effect was found in the study of learning and spatial
memory in rodents using the Morris water maze, which
indicated the involvement of hippocampal neurons [10].
However, the studies on humans, which examined the
effect of a choline-fortified diet for pregnant women on
the development of their children’s cognitive abilities,
produced conflicting data [15–17].
Another area of choline research is its
neuroprotective effect and impact on cognitive functions
in adults. Pharmaceutical choline-containing drugs are
often prescribed for pathologies of the nervous system.
The neuroprotective effects of choline alfoscerate and
cytidine-5’-diphosphocholine (citicoline) have been
proven in treating cognitive impairment associated
with trauma, vascular disorders, or neurodegenerative
diseases [18–21]. The studies of choline effect on
cognitive functions of healthy individuals in postnatal
ontogeny have yielded mixed results. For example,
memory tests on 1391 adult men and women without
cognitive impairment revealed a positive effect of
choline consumption, with similar results found for
cognitive tests on 2195 people aged 70–74 [22, 23].
Knott et al. examined the effect of a single dose of
citicoline in low and medium concentrations. They
found that the effect was determined by the initial level
of choline, i.e., the subjects with initially low levels of
choline had improved cognitive functions after citicoline
treatment [24].
According to another study, choline bitartrate
improved the accuracy (rather than the time) of
visual-motor task performance in students [25]. A
positive relationship was found between the plasma
choline content in 15-year-olds and their school
performance [26]. Other researchers, however, did not
observe a positive effect of short-term choline bitartrate
treatment on the memory function of students [27].
Studies on school and college students are especially
relevant. Childhood and adolescence are the periods
of life when the morphofunctional maturation of the
nervous system is combined with intensive cognitive
activity during schooling. Of paramount importance
therefore is nutrition that satisfies the plastic and
functional needs of the nervous system. Choline is one
of such nutrients. However, more research is needed to
clarify the relationship between choline and cognitive
functions in different age groups, including students.
We should also mention a potential negative
effect of high choline intake on human health. This
problem has been widely discussed in recent years due
to the existence of choline metabolic pathways with
the participation of intestinal microflora. A certain
composition of intestinal microbiota produces a large
amount of trimethylamine (TMA), which is absorbed
by the epithelium, entering the liver through the portal
vein, where it is converted into trimethylamine N-oxide
(TMAO). The cumulative effects of TMAO are currently
associated with the risk of atherosclerosis, insulin
resistance, stomach and intestinal cancer, as well as
kidney pathology [28–30]. Therefore, increasing choline
intake should be recommended to adults with caution.
We aimed to expand our awareness of exogenous
choline effect on psychophysiological functions under
increased nervous stress. For this, we set the following
objectives:
– assessing levels of choline intake in university
students;
– analyzing the relationship between choline intake
levels and psychophysiological characteristics;
– studying the effect of choline supplementation on the
functional indicators of the central nervous system in
students in the pre-exam period.
STUDY OBJECTS AND METHODS
Study design. First, we formed a cohort of 87 study
subjects (13 males and 74 females) aged 19 from the
1st- and 2nd-year students of the Department of Social
Work and Psychology at Kemerovo State University
(Kemerovo, Russia) and obtained their informed
written consent to participate in the study. All the study
subjects were surveyed by questionnaire to assess their
dietary choline intake. In addition, they underwent
a psychophysiological examination to assess their
neurodynamic and cognitive functions.
Next, the 2nd-year students were divided into a
control and an experimental group, 20 people each
(4 males and 16 females) by pairwise selection based
on mechanical memory. The experimental group took a
mono-component dietary supplement “Choline 350 mg
Vegetable Capsules” (Solgar, USA). The supplement was
registered under No. RU.77.99.11.003.Е.004764.10.18
of 29.10.2018 in the Customs Union’s Register of State
Registration Certificates. Choline was taken for one
399
Tarasova O.L. et al. Foods and Raw Materials, 2021, vol. 9, no. 2, pp. 397–405
month, one capsule twice a day with a meal. At the end
of the intake period, both groups underwent another
psychophysiological examination.
Finally, the data were statistically processed and
analyzed.
Choline determination methods. Food frequency
questionnaire (FFQ) was used to determine the
frequency of consumption of choline-containing foods
and to estimate the absolute daily intake by portion
size [26]. The survey followed the Russian guidelinesI.
The questionnaire included foods with a choline
content of at least 10% of the daily intake per 100 g.
It also listed dairy products (milk, kefir) which had a
choline content of 5–8% of the daily intake per 100 g,
but could be consumed in fairly large amounts. The
subjects were surveyed in a group, with the interviewer
giving explanations about the questionnaire. The
respondents were asked to estimate the frequency
of consumption of the listed products during the last
month, as well as indicate the approximate size of the
portions. Then, we analyzed the responses to determine
the approximate amount of choline intake using
available sources [31, 32] and ranked the results by the
quartile method.
Methods for studying psychophysiological
functions. The neurodynamic and cognitive indicators
were determined with the psychophysiological complex
“Status PF”II. The testing was carried out in a group
in the university computer classroom on Tuesday and
Wednesday mornings before classes with minimum
extraneous irritants. Prior to the testing, we explained
its meaning and significance in order to form a positive
attitude among the study subjects. The tests that we
selected did not require significant mental strain or much
time to perform. In particular, we used the following
well-known diagnostic tools.
The latent period of a simple visual-motor reaction
is the most common psychomotor indicator that reflects
the rate of excitation along the reflex arc and, therefore,
the excitability of the central nervous system. This is
a rather labile indicator that adequately characterizes
its functional state. The general simple visual-motor
reaction time is determined by the subject’s anatomical
features of the sensory system, nervous processes,
psychophysiological state, and the motor-coordination
potential. The subjects were asked to press a key on the
computer keyboard as quickly as possible in response
to a light stimulus. The average time of a motor
reaction (ms) was determined after 30 light stimuli with
various random intervals.
I Martinchik AN, Baturin AK, Baeva VS. Razrabotka metoda
issledovaniya fakticheskogo pitaniya po analizu chastoty potrebleniya
pishchevykh produktov: sozdanie voprosnika i obshchaya otsenka
dostovernosti metoda [Developing a method to determine nutrition
by the frequency of food consumption: creating a questionnaire
and assessing the method’s reliability]. Problems of Nutrition.
1998;67(3):8–13. (In Russ.).
II Ivanov VI, Litvinova NA. Programma dlya EHVM “Otsenka
psikhofiziologicheskogo sostoyaniya organizma cheloveka (Status
PF)” [Computer program “Assessment of the psychophysiological
state of the human body (Status PF)”]; № 2001610233. 2001.
The latent period of a complex visual-motor reaction
reflects the time spent on analyzing information in the
integrative-triggering cortical zones and making a
decision about how to respond. The subjects were asked
to react to a red signal with their right hand, to a green
signal with their left hand, and not to react to a yellow
signal. The average time of a motor reaction (ms) was
determined after 30 light stimuli.
Functional mobility of nervous processes was
determined by the method of Khilchenko (1958)
modified by Makarenko et al. (1987). The level of
functional mobility is an indicator of neurodynamic
constitution that does not depend as much on the
actual functional state of the central nervous system
as the simple and complex sensorimotor reactions.
This method is based on a complex visual-motor
differentiation reaction in the feedback mode. In
contrast to the previous method, the intervals between
signals depended on the correctness of motor reactions,
decreasing by 20 ms after a correct reaction and
increasing after an incorrect one. The test included
120 standard stimuli. The test time (s) was a quantitative
level of functional mobility of the subject’s nervous
processes – the less time it took to do the test, the more
accurate the responses were. The accuracy of responses
was determined by the rate of changes between
excitation and inhibition, that is, the functional mobility
of nervous processes.
Balance of the nervous system in response a moving
object reflects the relationship between excitatory and
inhibitory processes in the cerebral cortex. This method
determines the accuracy of visual-motor reaction to an
object moving at the same speed in a circle. When the
object overlapped the marker on the circle, the subjects
had to press a key and “stop” it, with the time of
deviation between the object and the marker recorded up
to 1 ms. The subject’s reaction was considered accurate
if the deviation was within ± 5 ms. We recorded the
number of accurate reactions, anticipatory and lagging
reactions (total and average), as well as the average
deviation time.
Short-term visual memory is a phase of imprinting
characterized by a short storage of a limited number of
objects in memory. The stimuli on the monitor screen
included two-digit numbers (Ebbinghaus method),
syllables (Luria method), and unrelated words (Leser
method). They were presented one at a time for 1 s with
an interval of 2 s. The capacity of short-term memory
was determined by the number of correctly reproduced
stimuli immediately after presentation.
Attentional capacity was determined by the
maximum number of simultaneously perceived objects.
The subjects were shown a lined field (5 by 5), with
objects (crosses) randomly located in the cells. With
every exposure, the number of objects increased by one.
After a 500 ms exposure, the objects disappeared and
the subjects had to locate them on the field. Attentional
capacity was determined by the maximum number of
correctly located objects, expressed in points.
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Attention concentration was assessed with the
Schulte table presented on the monitor screen. The
subjects were to indicate the numbers from 1 to 25 in
ascending order. The time taken to complete the test was
an indicator of concentration. The less time one spent,
the higher their attention concentration.
Attentional set-shifting was assessed with a red
and black Schulte-Gorbov table. The subjects were
invited to indicate black numbers in ascending order
and red numbers in descending order: 1 – black,
24 – red, 2 – black, 23 – red, 3 – black, etc. The time
taken to complete the test was a measure of attentional
set-shifting (the less time, the better the indicator).
Attention stability was determined with a computer
version of the dot cancellation test. The subjects were
asked to look through lines of letters in the table and
mark the given four letters for 4 min. The test assessed
the speed of performance (number of letters viewed)
and its accuracy (number of errors), with their ratio
calculated as the total productivity index.
The HAM (health, activity, mood) testIII was used
for the students’ additional self-assessment of their
functional state. The questionnaire had 30 pairs of
subjective characteristics with opposite meanings (for
example, “funny-sad”, “slow-fast”, etc.). The subjects
were asked to indicate their current state on a scale
between these poles. The neutral state was marked as
“0” and the extreme (most pronounced) state as “3”
(both poles). The points were added up for each scale
(health, activity, and mood).
Statistical processing was carried out in Excel
and Statistica 6.0. Mean values and standard errors
were determined for all the indicators under study. In
addition, we performed the analysis of histograms and
the percentile analysis. Normality of the distribution
was measured by the Kolomogorov-Smirnov test. Due
to the small size of our sample, most indicators did not
III Doskin VA, Lavrentʹeva NA, Miroshnikov MP, Sharay VB. Test
differentsirovannoy samootsenki funktsionalʹnogo sostoyaniya
[A test for differentiated self-assessment of the functional state].
Voprosy Psychologii. 1973;19(6):141–145. (In Russ.).
have a normal distribution. Therefore, we applied the
Mann-Whitney test to compare two groups and the
median test for multiple comparisons. The Wilcoxon
rank test was used to assess changes in indicators. The
χ2 test measured the statistical significance of differences
in percentage ratios (P < 0.05). Spearman’s correlation
analysis was also applied.
RESULTS AND DISCUSSION
The food frequency questionnaire (FFQ) results
showed that the approximate level of choline intake
with the products included in the questionnaire ranged
from 100 to 900 mg per day (Fig. 1). We found that
60% of the respondents had a choline intake below the
recommended value (400 mg). The average choline
consumption was 448.7 ± 50.6 mg for males and
373.4 ± 21.6 mg for females, also below the recommended
value. Our data were generally consistent with
the results of various international studies, as reported
by Canadian authors [2]. Their review also emphasized
that the reported low intake of total choline did not
take into account its form (water-soluble or fat-soluble)
and did not always indicate its deficiency in the body.
When interpreting our results, we also assumed that the
actual intake of choline was higher than the level shown
by the FFQ, since the questionnaire did not include all
the foods consumed by students. Yet, we had enough
grounds for recommending that students who consume
less than 400 mg of choline per day adjust their diet by
including foods high in choline.
To study the relationship between neurodynamic
characteristics and choline intake, the students were
divided into three groups based on the quartile analysis:
a) low choline intake (under 240 mg/d), quartile 1;
b) medium choline intake (240–499 mg/d), quartiles 2
and 3; c) high choline intake (over 500 mg/d), quartile 4.
The comparison of the neurodynamic parameters in
these groups revealed some statistically significant
differences (Tables 1, 2).
We found that the students with a high choline intake
had the best indicators for functional mobility of nervous
0
5
10
15
20
25
30
100–200 200–300 300–400 400–500 500–600 600–700 700–800 800–900
%
Approximate choline intake, mg/day
200
220
240
260
280
300
320
April May Control group Latent period of a simple
visual-motor reaction, ms
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
April May April May
Control group Experimental group
Anticipatory reactions
to a moving object, ms
3
4
5
6
7
8
9
April May
Control group
April Experimental group
Points
Short-term memory for words Short-term memory for syllables
40
45
50
55
60
April May April May
Control group Experimental group
Points
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms
0
10
20
30
40
50
60
70
80
90
April Low Attention stability, productivity
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
April May April May
Low choline intake High choline intake
Short-term memory for
syllables, points
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
Figure 1 Students distribution by choline intake (according to the food frequency questionnaire)
0
5
10
15
20
25
30
100–200 200–300 300–%
Approximate 0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
April May April Control group Experimental Anticipatory reactions
to a moving object, ms
40
45
50
55
60
April May Control group Points
Самочувствие Активность 5.0
5.5
6.0
6.5
memory for
points
401
Tarasova O.L. et al. Foods and Raw Materials, 2021, vol. 9, no. 2, pp. 397–405
processes and the least time of lagging reactions to a
moving object.
The assessment of cognitive functions produced
quite unexpected results. The integral indicator of
attention stability based on the dot cancellation test
was the highest among students with a low choline
intake (Table 2). There were no other statistically
significant differences.
We found no statistically significant correlations
between dietary choline intake and psychophysiological
indicators in the sample as a whole. However, there were
significant differences in the groups with high, medium,
and low choline intake.
The group with a low choline intake showed
statistically significant correlations between the
choline value and the number of anticipatory reactions
to a moving object (r = 0.46, P < 0.05), the number of
accurate reactions (r = –0.68, P < 0.01), the average
time of lagging reactions to a moving object (r = 0.54,
P < 0.05), and the short-term memory for numbers
(r = –0.31, P < 0.05). Thus, the best indicators of
psychomotor accuracy and short-term memory were
found in students with the lowest choline intake.
In the group with a medium choline intake, its daily
value had a negative effect on the number of accurate
reactions to a moving object (r = –0.35, P < 0.05) and a
positive effect on the average deviation time in the same
test (r = 0.32, P < 0.05), just as in the low choline intake
group. We found no statistically significant correlations
between choline values and indicators of memory and
attention in this group.
The group with a high choline intake revealed
an inverse relationship between choline values and
the latent period of a simple visual-motor reaction
(r = –0.5, P < 0.05) and the time of completing the
attention concentration test (r = –0.4, P < 0.05), as well
as a direct relationship with attention stability (r = 0.45,
P < 0.05). This meant that those students who consumed
more choline in this group performed best in the visualmotor
reaction and attention tests.
Thus, we found that the level of dietary choline
intake had a greater effect on neurodynamic parameters
than on cognitive functions. Higher choline values
improved the mobility of nervous processes and
accuracy in complex visual-motor reactions. However,
their effects on cognitive functions were quite
contradictory. We assumed that our results should
be interpreted with other factors taken into account,
which affected the students’ choline intake and
psychophysiological state. Yet, these additional factors
were beyond the scope of this study.
The control and the experimental groups of 20
students in each were formed regardless of the choline
Table 2 Memory and attention parameters in students with different levels of choline intake
Cognitive functions Choline intake Mann-Whitney U-test
Low (1) Medium (2) High (3) 1–2 1–3 2–3
Short-term memory (numbers), points 6.3 ± 0.4 6.0 ± 0.3 5.8 ± 0.3
Short-term memory (words), points 7.13 ± 0.2 7.0 ± 0.3 7.1 ± 0.3
Short-term memory (syllables), points 4.4 ± 0.4 4.6 ± 0.3 4.7 ± 0.4
Attentional capacity, points 6.7 ± 0.4 6.5 ± 0.4 6.8 ± 0.4
Attention concentration test completion time, s 45.4 ± 2.7 47.4 ± 2.4 45.0 ± 2.3
Attentional set-shifting test completion time, s 173.4 ± 7.9 169.7 ± 5.6 167.2 ± 7.0
Attention stability: total productivity index 62.6 ± 5.5 45.9 ± 5.9 48.9 ± 6.4 0.03
*P < 0.05
Table 3 Choline intake in the control and experimental
groups, mg/day
Group Median
value
25–75
percentiles
Control (no choline treatment) 401 264–652
Experimental (with choline treatment) 416 315–492
Р (Mann-Whitney U-Test) 0.91
Tаble 1 Neurodynamic parameters in students with different levels of choline intake
Neurodynamic parameters Choline intake Mann-Whitney U-test*
Low (1) Medium (2) High (3) 1–2 1–3 2–3
Latent period of a simple visual-motor reaction, ms 292.3 ± 8.7 303.1 ± 23.3 279.6 ± 7.3
Latent period of a complex visual-motor reaction, ms 446.5 ± 17.2 444.1 ± 10.6 435.6 ± 9.6
Functional mobility of nervous processes – time, s 66.6 ± 1.8 65.2 ± 1.2 63.1 ± 1.3 0.04
Reaction to a moving object: average deviation from
accurate reactions, ms
29.8 ± 2.4 27.9 ± 3.3 30.4 ± 5.8
Reaction to a moving object: total anticipatory reactions, ms 297.1 ± 52.0 246.5 ± 75.4 298.2 ± 62.5
Reaction to a moving object: total lagging reactions, ms 513.6 ± 66.9 519.5 ± 51.4 340.4 ± 62.6 0.04 0.04
*P < 0.05
402
Tarasova O.L. et al. Foods and Raw Materials, 2021, vol. 9, no. 2, pp. 397–405
values. The analysis of their dietary choline intake did
not show any statistically significant differences between
the groups (Table 3).
Thus, the control and the experimental groups, which
had a homogeneous age and sex composition and similar
cognitive indicators at the beginning of the study, were
also quite similar in choline intake.
However, we identified some statistically significant
changes in their neurodynamic parameters during the
observation period (Figs. 2–5).
The experimental group showed significant
improvements in the simple visual-motor reaction
times (Fig. 2) within a month. The number of
anticipatory reactions to a moving object decreased
in the experimental group, but increased in the control
group (Fig. 3). The students who received choline
supplementation had better short-term memory
for words and syllables. However, their attentional
capacity remained the same, decreasing in the control
group (Fig. 4).
The HAM (health, activity, mood) method revealed
that during the second examination, the students taking
choline supplements had significantly higher indicators
of health and activity, compared to the control group
(Fig. 5). Thus, the students in the experimental group
were in a better state of health.
These changes showed that the pre-exam stress did
not affect the functional state of the central nervous
system of students in the experimental group – in fact,
it improved.
Next, we divided the experimental group into two
subgroups, depending on the level of choline intake:
students with choline intake below the median value
(416 mg) and students with choline intake above the
median value. Thus, we could assess the effect of
choline supplementation, taking into account the
students’ dietary choline intake.
Figure 2 Changes in the simple visual-motor reaction times
(Р < 0.05)
Figure 4 Changes in short-term memory and attention indicators (Р < 0.05)
Figure 5 Changes in the HAM (health, activity, mood) test
(Р < 0.05)
700–800 800–900
200
220
240
260
280
300
320
April May April May
Control group Experimental group
Latent period of a simple
visual-motor reaction, ms
May
Control group
April May
Experimental group
memory for words Short-term memory for syllables
April May April May
Low choline
intake
High choline
intake
0
10
20
30
40
50
60
70
80
90
April May April May
Low choline intake High choline intake
Attention stability, productivity
22
12 11
5
1 5
3
3
neurodynamic
cognitive
neurodynamic and cognitive
100–200 200–300 300–400 400–500 500–600 600–700 Approximate choline intake, mg/day
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
April May April May
Control group Experimental group
Anticipatory reactions
to a moving object, ms
3
4
5
6
7
8
9
April Points
Short-term 40
45
50
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60
April May April May
Control group Experimental group
Points
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
Latent period of a simple
visual-motor reaction, ms
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
April May April May
Low choline intake High choline intake
Short-term memory for
syllables, points
0
5
10
15
20
25
30
14
3
HAM-HAM-HAM- Figure 3 Changes in reactions to a moving object (Р < 0.05)
100–200 200–300 300–400 400–500 500–600 600–700 700–800 800–900
Approximate choline intake, mg/day
200
220
240
260
280
300
320
April May April May
Control group Experimental group
Latent period of a simple
visual-motor reaction, ms
April May April May
Control group Experimental group
3
4
5
6
7
8
9
April May
Control group
April May
Experimental group
Points
Short-term memory for words Short-term memory for syllables
April May April May
Control group Experimental group
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms
0
10
20
30
40
50
60
70
80
90
April May April May
Low choline intake High choline intake
Attention stability, productivity
April May April May
Low choline intake High choline intake
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
0
5
10
15
20
25
30
100–200 200–300 300–400 400–500 500–600 600–700 700–800 800–900
%
Approximate choline intake, mg/day
200
220
240
260
280
300
320
April May April May
Control group Experimental group
Latent period of a simple
visual-motor reaction, ms
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
April May April May
Control group Experimental group
Anticipatory reactions
to a moving object, ms
3
4
5
6
7
8
9
April May
Control group
April May
Experimental group
Points
Short-term memory for words Short-term memory for syllables
40
45
50
55
60
April May April May
Control group Experimental group
Points
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms 0
10
20
30
40
50
60
70
80
90
April May April Low choline intake High Attention stability, productivity
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
April May April May
Low choline intake High choline intake
Short-term memory for
syllables, points
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
Health Activity Mood
403
Tarasova O.L. et al. Foods and Raw Materials, 2021, vol. 9, no. 2, pp. 397–405
Figure 6 Changes in the simple visual-motor reaction times
in students with choline supplementation vs. initial choline
intake (Р < 0.05)
Figure 7 Changes in attention stability in students with
choline supplementation vs. initial choline intake (Р < 0.05)
We found statistically significant changes in
neurodynamic parameters among students from the
experimental group with a low choline intake. In
particular, they showed a shorter simple visual-motor
reaction time (Fig. 6) and improved attention stability
(Fig. 7).
Changes in cognitive functions indicated better
short-term memory for syllables in all experimental
students, regardless of their choline intake, and
improved performance in the dot cancellation test only
in those with a low choline intake (Fig. 8).
The self-assessment with the HAM (health, activity,
mood) method did not reveal any significant trends
associated with levels of choline intake.
In order to obtain more general information about
how choline supplementation affected the functional
state of the central nervous system, we analyzed
correlations between different psychophysiological
parameters throughout the study. The closer
connectedness between various neurodynamic,
cognitive, and subjective indicators was regarded as a
sign of increased psychophysiological adaptation in the
pre-exam period.
Figure 9 shows changes in the number of statistically
significant correlations between various indicators in
the control and experimental groups. We can see a clear
difference in the number of correlations between the
control and experimental groups, indicating a lesser
degree of cognitive stress in the students who took
choline supplements.
Thus, we found a positive effect of choline
supplementation on the psychophysiological indicators
of students in the stressful pre-exam period. Yet,
some of the results were quite ambiguous and even
conflicting: for example, negative correlations between
background choline intake and attention indicators in
both the control and the experimental groups, or general
uselessness of choline supplementation for cognitive
functions. As we know, a human need for choline and
sensitivity to its deficiency are highly variable and
genetically determined by heterogeneous metabolic
pathways of endogenous synthesis and dietary choline
conversion.
Our study showed that choline supplementation can
be recommended to students, especially those with a low
consumption of choline-rich foods.
May
Experimental group
3
April May
Control group
April May
Experimental group
Short-term memory for words Short-term memory for syllables
April May
Experimental group
Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms
0
10
20
30
40
50
60
70
80
90
April May April May
Low choline intake High choline intake
Attention stability, productivity
May
choline intake
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
May April May
group Experimental group
3
April May
Control group
April May
Experimental group
Short-term memory for words Short-term memory for syllables
May April May
group Experimental group
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms
0
10
20
30
40
50
60
70
80
90
April May April May
Low choline intake High choline intake
Attention stability, productivity
May April May
choline intake High choline intake
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
Figure 8 Changes in short-term memory in students with
choline supplementation vs. initial choline intake (Р < 0.05)
Figure 9 Statistically significant correlations between
psychophysiological indicators in different groups
0
5
10
15
20
25
30
100–200 200–300 300–400 400–500 500–600 600–700 700–800 800–900
%
Approximate choline intake, mg/day
200
220
240
260
280
300
320
April May April May
Control group Experimental group
Latent period of a simple
visual-motor reaction, ms 0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
April May April May
Control group Experimental group
Anticipatory reactions
to a moving object, ms
3
4
5
6
7
8
9
April May
Control group
April May
Experimental group
Points
Short-term memory for words Short-term memory for syllables
40
45
50
55
60
April May April May
Control group Experimental group
Points
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms
0
10
20
30
40
50
60
70
80
90
April May Low choline intake Attention stability, productivity
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
April May April May
Low choline intake High choline intake
Short-term memory for
syllables, points
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
0
5
10
15
20
25
30
100–200 200–300 300–400 400–500 500–600 600–700 700–800 800–900
%
Approximate choline intake, mg/day
200
220
240
260
280
300
320
April May April Control group Experimental Latent period of a simple
visual-motor reaction, ms 0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
April May April May
Control group Experimental group
Anticipatory reactions
to a moving object, ms
3
4
5
6
7
8
9
April May
Control group
April May
Experimental group
Points
Short-term memory for words Short-term memory for syllables
40
45
50
55
60
April May April May
Control group Experimental group
Points
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms
0
10
20
30
40
50
60
70
80
90
April Low choline Attention stability, productivity
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
April May April May
Low choline intake High choline intake
Short-term memory for
syllables, points
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
April May April May
Control group Experimental group
0
5
10
15
20
25
30
100–200 200–300 300–400 400–500 500–600 600–700 700–800 800–900
%
Approximate choline intake, mg/day
200
220
240
260
280
300
320
April May April Control group Experimental Latent period of a simple
visual-motor reaction, ms
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
April May April May
Control group Experimental group
Anticipatory reactions
to a moving object, ms
3
4
5
6
7
8
9
April May
Control group
April May
Experimental group
Points
Short-term memory for words Short-term memory for syllables
40
45
50
55
60
April May April May
Control group Experimental group
Points
Самочувствие Активность Настроение
200
220
240
260
280
300
320
340
April May April May
Low choline
intake
High choline
intake
Latent period of a simple
visual-motor reaction, ms
0
10
20
30
40
50
60
70
80
90
April Low choline Attention stability, productivity
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
April May April May
Low choline intake High choline intake Short-term memory for
syllables, points
0
5
10
15
20
25
30
14
22
12 11
5
1 5
3
3
3
HAM-neurodynamic
HAM-cognitive
HAM- neurodynamic and cognitive
404
Tarasova O.L. et al. Foods and Raw Materials, 2021, vol. 9, no. 2, pp. 397–405
CONCLUSION
Half of the students had a dietary choline intake
below the recommended value. The levels of choline
intake had a greater effect on the neurodynamic
parameters than on the cognitive functions. Increased
choline intake correlated with higher functional mobility
of nervous processes and faster reactions to a moving
object. The students who took choline supplements for
one month had positive changes in the functional state
of the central nervous system, compared to the control
group. Besides, these changes were more pronounced
in those students who had a low intake of dietary
choline. An additional daily intake of 700 mg choline
supplements can be recommended to students under
pre-exam stress, especially those with a dietary choline
deficiency, to improve the functional state of their
central nervous system. However, we did not assess the
effectiveness of smaller amounts of choline. We believe
there is no need for continuous choline supplementation,
since current research indicates possible negative health
effects.
CONTRIBUTION
The authors were equally involved in preparing the
manuscript.
CONFLICT OF INTEREST
The authors declare that there is not conflict of
interest.
1. Wallace TC, Blusztajn JK, Caudill MA, Klatt KC, Natker E, Zeisel SH, et al. The underconsumed and underappreciated essential nutrient. Nutrition Today. 2018;53(6):240-253. https://doi.org/10.1097/NT.0000000000000302.
2. Wiedeman AM, Barr SI, Green TJ, Xu Z, Innis SM, Kitts DD. Dietary choline intake: Current state of knowledge across the life cycle. Nutrients. 2018;10(10). https://doi.org/10.3390/nu10101513.
3. Dietary Reference Intakes for Thiamin, Riboflavin, Niacin, Vitamin B6, Folate, Vitamin B12, Pantothenic Acid, Biotin, and Choline. Washington: National Academies Press; 1998. 592 p. https://doi.org/10.17226/6015.
4. Ganz AB, Klatt KC, Caudill MA. Common genetic variants alter metabolism and influence dietary choline requirements. Nutrients. 2017;9(8). https://doi.org/10.3390/nu9080837.
5. Sikalidis AK. From food for survival to food for personalized optimal health: A historical perspective of how food and nutrition gave rise to nutrigenomics. Journal of the American College of Nutrition. 2019;38(1):84-95. https://doi.org/10.1080/07315724.2018.1481797.
6. Bernhard W, Poets CF, Franz AR. Choline and choline-related nutrients in regular and preterm infant growth. European Journal of Nutrition. 2019;58(3):931-945. https://doi.org/10.1007/s00394-018-1834-7.
7. Van Echten-Deckert G, Alam S. Sphingolipid metabolism - an ambiguous regulator of autophagy in the brain. Biological Chemistry. 2018;399(8):837-850. https://doi.org/10.1515/hsz-2018-0237.
8. Zeisel SH. Choline, other methyl-donors and epigenetics. Nutrients. 2017;9(5). https://doi.org/10.3390/nu9050445.
9. Roth ED, Roth TL, Money KM, SenGupta S, Eason DE, Sweatt JD. DNA methylation regulates neurophysiological spatial representation in memory formation. Neuroepigenetics. 2015;2:1-8. https://doi.org/10.1016/j.nepig.2015.03.001.
10. Blusztajn JK, Slack BE, Mellott TJ. Neuroprotective actions of dietary choline. Nutrients. 2017;9(8). https://doi.org/10.3390/nu9080815.
11. Haam J, Yakel JL. Cholinergic modulation of the hippocampal region and memory function. Journal of Neurochemistry. 2017;142:111-121. https://doi.org/10.1111/jnc.14052.
12. Romberg C, Bartko S, Wess J, Saksida LM, Bussey TJ. Impaired object-location learning and recognition memory but enhanced sustained attention in M2 muscarinic receptor-deficient mice. Psychopharmacology. 2018;235(12):3495-3508. https://doi.org/10.1007/s00213-018-5065-7.
13. Janeczek M, Gefen T, Samimi M, Kim G, Weintraub S, Bigio E, et al. Variations in acetylcholinesterase activity within human cortical pyramidal neurons across age and cognitive trajectories. Cerebral Cortex. 2018;28(4):1329-1337. https://doi.org/10.1093/cercor/bhx047.
14. Zaharov DV, Mihailov VA, Kotsiubinskaya YuV. Choline Alphoscerat (Gliatilin) in the pathogenetic therapy of Alzheimer's disease. V.M. Bekhterev Review of Psychiatry and Medical Psychology. 2018;(4):93-99. (In Russ.). https://doi.org/10.31363/2313-7053-2018-4-93-99.
15. Cheatham CL, Goldman BD, Fischer LM, da Costa K-A, Reznick JS, Zeisel SH. Phosphatidylcholine supplementation in pregnant women consuming moderate-choline diets does not enhance infant cognitive function: A randomized, double-blind, placebo-controlled trial. American Journal of Clinical Nutrition. 2012;96(6):1465-1472. https://doi.org/10.3945/ajcn.112.037184.
16. Boeke CE, Gillman MW, Hughes MD, Rifas-Shiman SL, Villamor E, Oken E. Choline intake during pregnancy and child cognition at age 7 years. American Journal of Epidemiology. 2013;177(12):1338-1347. https://doi.org/10.1093/aje/kws395.
17. Wiedeman AM, Chau CMY, Grunau RE, McCarthy D, Yurko-Mauro K, Dyer RA, et al. Plasma betaine is positively associated with developmental outcomes in healthy toddlers at age 2 years who are not meeting the recommended adequate intake for dietary choline. Journal of Nutrition. 2018;148(8):1309-1314. https://doi.org/10.1093/jn/nxy108.
18. Gavrilova SI, Kolykhalov IV, Ponomareva EV, Fedorova YaB, Selezneva ND. Clinical efficacy and safety of choline alfoscerate in the treatment of late-onset cognitive impairment. S.S. Korsakov Journal of Neurology and Psychiatry. 2018;118(5):45-53. (In Russ.). https://doi.org/10.17116/jnevro20181185145.
19. Bekdash RA. Choline, the brain and neurodegeneration: insights from epigenetics. Frontiers in Bioscience - Landmark. 2018;23(6):1113-1143. https://doi.org/10.2741/4636.
20. Jasielski P, Piędel F, Piwek M, Rocka A, Petit V, Rejdak K. Application of citicoline in neurological disorders: A systematic review. Nutrients. 2020;12(10). https://doi.org/10.3390/nu12103113.
21. Putilina MV. A personalized selection of choline precursors in evidence - based medicine. S.S. Korsakov Journal of Neurology and Psychiatry. 2020;120(6):144-151. (In Russ.). https://doi.org/10.17116/jnevro2020120061144.
22. Poly C, Massaro JM, Seshadri S, Wolf PA, Cho E, Krall E, et al. The relation of dietary choline to cognitive performance and white-matter hyperintensity in the Framingham Offspring Cohort. American Journal of Clinical Nutrition. 2011;94(6):1584-1591. https://doi.org/10.3945/ajcn.110.008938.
23. Nurk E, Refsum H, Bjelland I, Drevon CA, Tell GS, Ueland PM, et al. Plasma free choline, betaine and cognitive performance: the Hordaland Health Study. British Journal of Nutrition. 2013;109(3):511-519. https://doi.org/10.1017/S0007114512001249.
24. Knott V, de la Salle S, Choueiry J, Impey D, Smith D, Smith M, et al. Neurocognitive effects of acute choline supplementation in low, medium and high performer healthy volunteers. Pharmacology Biochemistry and Behavior. 2015;131:119-129. https://doi.org/10.1016/j.pbb.2015.02.004.
25. Naber M, Hommel B, Colzato LS. Improved human visuomotor performance and pupil constriction after choline supplementation in a placebo-controlled double-blind study. Scientific Reports. 2015;5. https://doi.org/10.1038/srep13188.
26. Nilsson TK, Hurtig-Wennlöf A, Sjöström M, Herrmann W, Obeid R, Owen JR, et al. Plasma 1-carbon metabolites and academic achievement in 15-yr-old adolescents. FASEB Journal. 2016;30(4):1683-1688. https://doi.org/10.1096/fj.15-281097.
27. Lippelt DP, van der Kint S, van Herk K, Naber M. No acute effects of choline bitartrate food supplements on memory in healthy, young, human adults. PLoS One. 2016;11(6). https://doi.org/10.1371/journal.pone.0157714.
28. Egshatyan LV, Tkacheva ON, Boytsov SA. Gut enterobiota - A new player in atherosclerosis pathogenesis. Cardiovascular Therapy and Prevention. 2014;13(6):56-61. (In Russ.). https://doi.org/10.15829/1728-8800-2014-6-56-61.
29. Arias N, Arboleya S, Allison J, Kaliszewska A, Higarza SG, Gueimonde M, et al. The relationship between choline bioavailability from diet, intestinal microbiota composition, and its modulation of human diseases. Nutrients. 2020;12(8). https://doi.org/10.3390/nu12082340.
30. Gatarek P, Kaluzna-Czaplinska J. Trimethylamine N-oxide (TMAO) in human health. EXCLI Journal. 2021;20:301-319. https://doi.org/10.17179/excli2020-3239.
31. Patterson KY, Bhagwat AS, Williams JR, Howe JC, Holden JM. USDA database for the choline content of common foods. Release Two. Washington: U.S. Department of Agriculture; 2008. 37 p.
32. Tutelʹyan VA. Khimicheskiy sostav i kaloriynostʹ rossiyskikh produktov pitaniya [Chemical composition and calorie content of Russian food products]. Moscow: DeLi plyus; 2012. 283 p. (In Russ.).